Abstract
Leveraging social media to detect psychological stress is an emerging research topic, as it addresses one of the most common mental health issues. One of the notable challenges in this area, however, is data sparsity: users with high stress level tend to reduce their activities on social networks. While teenagers’ mobility behavior always appears some outliers for stress release in physical world, a question arises: can we identify the stress-related outlier features from daily trajectories to facilitate stress detection? In this paper, we propose a co-training-based semi-supervised learning approach that consists of two separated classifiers. One classifier is conditional random field (CRF), which takes outlier features from GPS trajectories as input to model the daily moving behavior correlation of stress. The other classifier is deep neural network (DNN) involving tweet features to model the social media behavior correlation of stress. We evaluate our approach with an over 6-month user study on 57 teenagers from Beijing, and demonstrate effectiveness of the proposed model compared to state-of-the-art methods .
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Acknowledgement
The work is supported by National Natural Science Foundation of China (61373022, 61532015, 71473146) and Chinese Major State Basic Research Development 973 Program (2015CB352301).
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Jin, L., Xue, Y., Li, Q., Feng, L. (2016). Integrating Human Mobility and Social Media for Adolescent Psychological Stress Detection. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_23
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